The first one is the relatively high centrality of in-distribution data in representation spaces.
Since BatchNorm is periodically assembled in many DNN models, the latent features should be surrounded in the center of the corresponding representation space.
Taking the ResNet-34 model trained on CIFAR-10 as an example,
we randomly select 1,000 samples from in-distribution and three out-of-distribution testing datasets, respectively.
Then, we fetch the latent features in a random hidden layer, and visualize the features via t-SNE [1]}.As shown in Fig. REF , the in-distribution dataset is clearly separated from other OOD datasets, with relatively centralized in the reduced two-dimensional space.
